Comparison of the performance of multi-layer perceptron and linear regression for epidemiological data
From MaRDI portal
Publication:956790
DOI10.1016/S0167-9473(02)00257-8zbMath1429.62645OpenAlexW2055000402WikidataQ57966217 ScholiaQ57966217MaRDI QIDQ956790
Laetitia Huiart, Jean Gaudart, Bernard Giusiano
Publication date: 26 November 2008
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/s0167-9473(02)00257-8
Linear regression; mixed models (62J05) Applications of statistics to environmental and related topics (62P12) Neural nets and related approaches to inference from stochastic processes (62M45)
Related Items (4)
Local minima in categorical multiple regression ⋮ An extension of the quadratic error function for learning imprecise data in multivariate nonlinear regression ⋮ Recurrent support vector regression for a non-linear ARMA model with applications to forecasting financial returns ⋮ Valid hypothesis testing in face of spatially dependent data using multi-layer perceptrons and sub-sampling techniques
Cites Work
- A comparison between neural networks and chaotic models for exchange rate prediction.
- Neural networks and logistic regression: Part I
- Neural networks and logistic regression. II.
- AR parameter estimation by a feedback neural network
- Neural networks and statistical inference: seeking robust and efficient learning
- Feedforward neural networks for principal components extraction.
- Comparison of the performance of neural network methods and Cox regression for censored survival data.
- Neural networks: A review from a statistical perspective. With comments and a rejoinder by the authors
- Multilayer feedforward networks are universal approximators
- An introduction to model selection
- Prediction Intervals for Artificial Neural Networks
- Incremental Least Squares Methods and the Extended Kalman Filter
- Unnamed Item
- Unnamed Item
- Unnamed Item
This page was built for publication: Comparison of the performance of multi-layer perceptron and linear regression for epidemiological data
